File Processing Research Articles

Programmer Productivity Enhancement Through Controlled Natural Language Input

We have created CABERNET, a Controlled Nature Language (CNL) based approach to program creation. CABERNET allows programmers to use a simple outline-based syntax. This allows increased programmer efficiency and syntax flexibility. CNLs have successfully been used for writing requirements documents. We propose taking this approach well beyond this to fully functional programs. Through the use of heuristics and inference to analyze and determine the programmer’s intent we are able to create fully functional mobile applications. The goal is for programs to be aligned with the way that the humans think rather than the way computers process information. Through the use of templates a CABERNET application can be processed to run on multiple run time environments. Because processing of a CABERNET program file results in native application program performance is maintained.

IMPLEMENTATION OF INVESTMENT FUNDS SHARING AGREEMENT IN SHARIA BANKING SYSTEM IN INDONESIA

Mudharabah financing investment, also known as trust financing, is a method of distributing funds in Sharia banking to comply with the religious prohibition of interest on loans. However, the use of legal protection has not been maximized in increasing the growth of mudharabah financing investment products, especially for SMEs (Micro, Small and Medium Enterprises), due to the complicated process of filing and guarantees needed by the bank. This study, therefore aims to implement adequate investment funds sharing agreement in Indonesia’s Sharia Banking System for mudharabah investments to be felt in all categories. The result showed that regulatory restructuring is needed for the application of mudharabah investment to be a real sector driver without eliminating the purity of Islamic principles.

PRE-PROCESSING OF ADDITIVE MANUFACTURING INPUT FILES FOR NUMERICAL SIMULATION

In this contribution, we present the concept of a 3D printer software emulator facilitating the creation of a spatial finite element mesh suitable for the printing process simulation. The concept is based on gradual processing of a native 3D printer input file (in a G-code format). This file contains a complete description of manufacturing process consisting of series of individual commands interpreted by a printer. The effect of each command needs to be precisely evaluated to obtain the position of the printer head and the volume of the deposited material in any given time. The calculation is performed in the same way as in the Marlin printer firmware using the trapezoidal motion curves and a command buffer. To represent a computational model, a discrete voxel model with variable edge length and time discretization is used. The volume of deposited material is calculated for each voxel as a function of time. The resulting model is suitable for numerical analysis of the printing process.

Connecting EEG signal decomposition and response selection processes using the theory of event coding framework.

The neurophysiological mechanisms underlying the integration of perception and action are an important topic in cognitive neuroscience. Yet, connections between neurophysiology and cognitive theoretical frameworks have rarely been established. The theory of event coding (TEC) details how perceptions and actions are associated (bound) in a common representational domain (the “event file”), but the neurophysiological mechanisms underlying these processes are hardly understood. We used complementary neurophysiological methods to examine the neurophysiology of event file processing (i.e., event‐related potentials [ERPs], temporal EEG signal decomposition, EEG source localization, time‐frequency decomposition, EEG network analysis). We show that the P3 ERP component and activity modulations in inferior parietal regions (BA40) reflect event file binding processes. The relevance of this parietal region is corroborated by source localization of temporally decomposed EEG data. We also show that temporal EEG signal decomposition reveals a pattern of results suggesting that event file processes can be dissociated from pure stimulus and response‐related processes in the EEG signal. Importantly, it is also documented that event file binding processes are reflected by modulations in the network architecture of theta frequency band activity. That is, when stimulus–response bindings in event files hamper response selection this was associated with a less efficient theta network organization. A more efficient organization was evident when stimulus–response binding in event files facilitated response selection. Small‐world network measures seem to reflect event file processing. The results show how cognitive‐theoretical assumptions of TEC can directly be mapped to the neurophysiology of response selection.

CCMod: a GATE module for Compton camera imaging simulation

Compton cameras are gamma-ray imaging systems which have been proposed for a wide variety of applications such as medical imaging, nuclear decommissioning or homeland security. In the design and optimization of such a system Monte Carlo simulations play an essential role. In this work, we propose a generic module to perform Monte Carlo simulations and analyses of Compton Camera imaging which is included in the open-source GATE/Geant4 platform. Several digitization stages have been implemented within the module to mimic the performance of the most commonly employed detectors (e.g. monolithic blocks, pixelated scintillator crystals, strip detectors...). Time coincidence sorter and sequence coincidence reconstruction are also available in order to aim at providing modules to facilitate the comparison and reproduction of the data taken with different prototypes. All processing steps may be performed during the simulation (on–the–fly mode) or as a post–process of the output files (offline mode). The predictions of the module have been compared with experimental data in terms of energy spectra, angular resolution, efficiency and back-projection image reconstruction. Consistent results within a 3-sigma interval were obtained for the energy spectra except for low energies where small differences arise. The angular resolution measure for incident photons of 1275 keV was also in good agreement between both data sets with a value close to 13°. Moreover, with the aim of demonstrating the versatility of such a tool the performance of two different Compton camera designs was evaluated and compared.

IoT Based Atmosphere Monitoring System using Hadoop Map Reduce Paradigm

In today’s world there are a host of serious environmental problems, and air pollution is one of the top causes of many chronic respiratory diseases with accelerated aging of lungs and possibly cancer. Air pollution is the most widespread pollution which is inevitable, since air being an ever pervading medium and carrier, which can transfer the pollutants very fast in no time. It is generally caused due to pollutants like carbon dioxide, carbon monoxide, nitrogen oxides, sulfur dioxide that are released especially when fuels are burnt.This paper aims at designing an Internet of Things (IoT) based atmosphere monitoring system using Hadoop ecosystem. Internet of things (IoT) provides a platform that allows devices to be connected, sensed and be controlled remotely across a network infrastructure. Hadoop is an open source framework which is used to store and process large amount of data using commodity hardware. The key feature of this paper is to measure the level of air contaminants, temperature in the atmosphere using sensors, and also store the sensor data into the cloud server that is Adafruit. Thereby, we redirect the data into Hadoop Distributed File System (HDFS) and process the sensor data to list out the areas with highest pollution level and temperature.

A context sensitive security framework for Enterprise multimedia placement in fog computing environment

In the era of ICT, multimedia files are one of the main sources of information sharing for any enterprise located in one or more geographical locations. Online video watching and editing platforms needs to store the multimedia file close to the end user so that the latency can be minimized which in result enhances the quality of experience. Fog computing is evolved as distributed computing infrastructure located close to end user with minimum latency. As, Fog computing is distributed and can be owned by third party providers, a framework is proposed which selects the appropriate Fog computing environment for placement of multimedia files based on context and security requirements. Deep neural network is used to evaluate context parameters, explicit security requirement, file type classification, and final allocation decision. The proposed framework is tested using Juypter notebook and Python 3.6 framework for one million instances of multimedia files. It has received 84% (average of ten experimental runs) accuracy in selection of appropriate Fog layer to place a multimedia file. The Proposed framework enhances the multimedia file placement on Fog computing environment so that processing of file can be done without worrying about the security of Fog.

ELECTRONIC DICTIONARY OF SPORTS METAPHORS AS AN INSTRUMENT TO DEVELOP THE UKRAINIAN COMPUTER LEXICOGRAPHY

The article provides guidelines for studying the discipline Object-Oriented Programming for the training of specialists of the first (bachelor's) level of higher education in the field of knowledge 01 Education specialty 014 Secondary education (computer science) educational program (computer science, mathematics). This discipline plays a particularly important role in the training of computer science teachers, because the field of computer program development using the latest technologies, which includes object-oriented programming, is an important component of scientific and technological progress.Students are invited to consider the basic features of object-oriented programming and the principles of a visual approach to software design in an integrated environment Visual Studio.Net using Visual C#. The choice of Visual Studio.Net is due to the fact that the version of Visual Studio.Net Community is a free full-featured version, which differs from the professional only slightly limited features.The article considers the method of studying the basics and principles of object-oriented programming by demonstrating typical examples in lectures and consolidating the studied material in laboratory work. The examples are selected in such a way that it is possible to demonstrate the features of data input and output in the Visual C# visual programming environment, event programming, file processing, implementation of encapsulation, inheritance and polymorphism mechanisms. Simultaneously with the demonstration of the implementation of typical examples, the required properties of the control elements used in the project implementation are studied.43By performing laboratory work, students consolidate the knowledge gained in lectures and acquire practical skills of project development using Visual C#. The laboratory work ends with the creation of a multi-window project, which represents a computer training system for studying computational methods. The creation of such a project allows students to consolidate the theoretical knowledge and practical skills of program development and demonstrates the use of interdisciplinary links in the educational process.

Programmer Productivity Enhancement Through Controlled Natural Language Input

We have created CABERNET, a Controlled Nature Language (CNL) based approach to program creation. CABERNET allows programmers to use a simple outline-based syntax. This allows increased programmer efficiency and syntax flexibility. CNLs have successfully been used for writing requirements documents. We propose taking this approach well beyond this to fully functional programs. Through the use of heuristics and inference to analyze and determine the programmer’s intent we are able to create fully functional mobile applications. The goal is for programs to be aligned with the way that the humans think rather than the way computers process information. Through the use of templates a CABERNET application can be processed to run on multiple run time environments. Because processing of a CABERNET program file results in native application program performance is maintained.

Intelligent Bilingual Data Extraction and Rebuilding Using Data Mining for Big Data

In today’s World there exists various source of data in various formats (file formats), different structure, different types and etc. which is a hug collection of unstructured over the internet or social media. This gives rise to categorization of data as unstructured, semi structured and structured data. Data that exist in irregular manner without any particular schema are referred as unstructured data which is very difficult to process as it consists of irregularities and ambiguities. So, we are focused on Intelligent Processing Unit which converts unstructured big data into intelligent meaningful information. Intelligent text extraction is a technique that automatically identifies and extracts text from file format. The system consists of different stages which include the pre-processing, keyphase extraction techniques and transformation for the text extraction and retrieve structured data from unstructured data. The system consists multiple method/approach give better result. We are currently working in various file formats and converting the file format into DOCX which will come in the form of the un-structure Form, and then we will obtain that file in the structure form with the help of intelligent Pre-processing. The pre-process stages that triggers the unstructured data/corpus into structured data converting into meaning full. The Initial stage is the system remove the stop word, unwanted symbols noisy data and line spacing. The second stage is Data Extraction from various sources of file or types of files into proper format plain text. The then in third stage we transform the data or information from one format to another for the user to understand the data. The final step is rebuilding the file in its original format maintaining tag of the files. The large size files are divided into sub small size file to executed the parallel processing algorithms for fast processing of larger files and data. Parallel processing is a very important concept for text extraction and with its help; the big file breaks in a small file and improves the result. Extraction of data is done in Bilingual language, and represent the most relevant information contained in the document. Key-phase extraction is an important problem of data mining, Knowledge retrieval and natural speech processing. Keyword Extraction technique has been used to abstract keywords that exclusively recognize a document. Rebuilding is an important part of this project and we will use the entire concept in that file format and in the last, we need the same format which we have done in that file. This concept is being widely used but not much work of the work has been done in the area of developing many functionalities under one tool, so this makes us feel the requirement of such a tool which can easily and efficiently convert unstructured files into structured one.

Saksham: Resource Aware Block Rearrangement Algorithm for Load Balancing in Hadoop

Big Data Analytics demands huge processing power. Distributed computing seems to be a viable solution with the usage of commodity computers. But, processing big data optimally, on distributed systems poses the number of challenges, such as seamlessly integrating different resources of distributed systems. Hadoop provides this ease and hence is one of the most commonly used tools for the processing of big data. But, it is observed that the performance of Hadoop is compromised, when it comes to the heterogeneous environment. This paper proposes Saksham: The block rearrangement algorithm which optimizes the processing time by efficient file system management in the heterogeneous environment. Our proposed scheme successfully optimizes the big data processing over the homogeneous and heterogeneous environment. To achieve better performance for big data processing, we target two important aspects of heterogeneous distributed computing: file system management and process management. First, file system management basically controls the block placement and allows us to rearrange the blocks on specified nodes, considering the node’s storage and processing capability. Second, we use the concept of node labeling and scheduling to achieve better process management. The results prove that the proposed approach has optimized job execution time along with reduced latency and data-skew substantially.

Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model

Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected society where all things are connected and exchange information through the Internet. IoT technology is fused with 5G and artificial intelligence (AI) technologies for use various fields such as the smart city and smart factory. As the demand for IoT technology increases, security threats against IoT infrastructure, applications, and devices have also increased. A variety of studies have been conducted on the detection of IoT malware to avoid the threats posed by malicious code. While existing models may accurately detect malicious IoT code identified through static analysis, detecting the new and variant IoT malware quickly being generated may become challenging. This paper proposes a dynamic analysis for IoT malware detection (DAIMD) to reduce damage to IoT devices by detecting both well-known IoT malware and new and variant IoT malware evolved intelligently. The DAIMD scheme learns IoT malware using the convolution neural network (CNN) model and analyzes IoT malware dynamically in nested cloud environment. DAIMD performs dynamic analysis on IoT malware in a nested cloud environment to extract behaviors related to memory, network, virtual file system, process, and system call. By converting the extracted and analyzed behavior data into images, the behavior images of IoT malware are classified and trained in the Convolution Neural Network (CNN). DAIMD can minimize the infection damage of IoT devices from malware by visualizing and learning the vast amount of behavior data generated through dynamic analysis.

3D scan process optimisation study for rapid virtualization.

Introduction – Many of the problems facing 3d scanning as a digitisation method around the human form are caused by the time it takes to scan the entity. This can be solved by using multiple cameras organised in a way to scan the extremity simultaneously from multiple directions. This paper is the exploration around the minimum number of cameras needed to obtain a usable model.Methodology – Using a 5-stage experimental process for 17 subjects and batch processing each stage, determined the most efficient workflow.Results – Excluding the exploration subject, it was found that the use of 4 cameras simultaneously was 5.5 times faster, including processing time then it was to use a single camera.Conclusion – using multiple cameras makes the process 5.5 times faster, as well as batch processing, and having a standardised method to enable the use of algorithmic file processing.

시각동기 페이저 데이터를 이용한 재생발전원 분석

In recent years, solar and wind power have been determined to be the main generation sources of power system. Therefore, it is expected that the power system capacity of RES(Renewable Energy Sources) with volatility will increase. Accordingly, there is a need for system planning, monitoring, and operation of a RES linked power system using precise real-time data rather than conventional SCADA based data.BR This paper is a part of the development of the real-time monitoring/analysis/control system for the new and renewable power sources installed for the monitoring and analysis of Gangwon region where the grid connection capacity is increasing. First, we briefly introduce the software of GPS-based time-synchro phasor-based monitoring and analysis system installed in Gangwon region. The file processing of the collected PMU data is described. Finally, we will analyze the power system effect of RES linked to Gangwon region using time-synchro phasor data.

Case file analyses in child protection research: Review of methodological challenges and development of a framework

In cases of child maltreatment, many different professional groups and institutions are involved. This involvement, in general, leads to the creation of documents, which are commonly referred to as case files, charts, or administrative data. Although their original aim is to facilitate communication, document procedures, and legitimize actions, case files are frequently used in research on various issues related to child protection. However, no methodological framework for conducting case file analysis exists to date. First, the article proposes a three-step model for the process of case file keeping, including aspects on the level of information gathering, processing, and presentation. Second, the different types of research questions investigated and the methods used in case file analyses in child protection research are shown based on the review of 219 scientific articles. Third, drawing on the first two parts, challenges of case file analysis in child protection research are summarized following different steps of the research process. Recommendations for the use of case files in child protection research are given.

Spell: Online Streaming Parsing of Large Unstructured System Logs

System event logs have been frequently used as a valuable resource in data-driven approaches to enhance system health and stability. A typical procedure in system log analytics is to first parse unstructured logs to structured data, and then apply data mining and machine learning techniques and/or build workflow models from the resulting structured data. Previous work on parsing system event logs focused on offline, batch processing of raw log files. But increasingly, applications demand online monitoring and processing. As a result, a streaming method to parse unstructured logs is needed. We propose an online streaming method $\mathsf{Spell}$Spell, which utilizes a longest common subsequence based approach, to parse system event logs. We show how to dynamically extract log patterns from incoming logs and how to maintain a set of discovered message types in streaming fashion. An enhancement to find more accurate message types is also proposed. We also propose and evaluate a method to automatically discover semantic meanings for parameter fields identified by Spell. We compare $\mathsf{Spell}$Spell against state-of-the-art methods to extract patterns from system event logs on large real data. The results demonstrate that, compared with other log parsing alternatives, $\mathsf{Spell}$Spell shows its superiority in terms of both efficiency and effectiveness.

Design of an experimental breeder reactor run 138B reactor physics benchmark evaluation management application

ABSTRACT Reactor Monte Carlo simulations present numerous challenges including input file generation and processing output files for relevant results. Creating an input file for a heterogeneous core is further complicated when using depleted fuel. Performing perturbations to geometry and material characteristics exacerbates the challenge due to human errors if the input files are manually generated. The difficulty of generating input files is often encountered during the process of performing a reactor physics benchmark evaluation. These challenges were encountered when performing a reactor physics benchmark evaluation for the Experimental Breeder Reactor II (EBR-II), a sodium cooled fast reactor. The EBR-II Monte Carlo input file uses more than 1,000 unique material specifications and ~8,000 regions. To address the input file generation challenge, a computer application was designed to automate the input file generation process. The application utilizes user input from a graphical user interface in conjunction with spreadsheets containing material and geometric data to generate Monte Carlo input files. The application, called MICKA, was used to generate input files to support the reactor physics benchmark evaluation for EBR-II. This paper describes the reactor configuration and provides information about the application design and capabilities for addressing challenges often encountered in reactor physics benchmark evaluations.

Characterization of Leptazolines A-D, Polar Oxazolines from the Cyanobacterium Leptolyngbya sp., Reveals a Glitch with the "Willoughby-Hoye" Scripts for Calculating NMR Chemical Shifts.

The bioactivity-guided examination of a Leptolyngbya sp. led to the isolation of leptazolines A-D (1-4), from the culture media, along with two degradation products (5 and 6). Density functional theory nuclear magnetic resonance calculations established the relative configurations of 1 and 2 and revealed that the calculated shifts depended on the operating system when using the "Willoughby-Hoye" Python scripts to streamline the processing of the output files, a previously unrecognized flaw that could lead to incorrect conclusions.

Can Pre-Commitment Increase Savings Deposits? Evidence from a Tax Time Field Experiment

This paper presents the results of an experiment testing the roles of a savings pre-commitment and different savings-focused choice architectures on the savings deposit decisions of 845,786 low- and moderate-income (LMI) tax filers. Results suggest that pre-committing to save at the start of the tax filing process can, among certain populations, dramatically increase savings rates. Among early tax filers, pre-commitment is associated with a 20.6 percentage point increase in savings deposits and a $418.86 increase in the amount deposited to savings. We observe more modest effects of pre-commitment on a general sample of tax filers. We also see strong evidence that choice architectures emphasizing savings strongly impact the deposit decisions of tax filers. The experiment also revealed cautionary evidence that the structure of pre-commitment can solidify decisions, making it then harder to later nudge those who opt-out of savings to change their minds. These findings may be broadly applicable to settings beyond the tax time moment – such as to applications that seek to encourage particular behaviors (like work or exercise) on the part of its participants.

Likelihood Construction of Hawkes Process in Insurance Claim Settlement Process

The construction of the likelihood equation in the point process models plays a very important role, especially to obtain a good estimator of the parameters of a model. This paper aims to construction of the likelihood of Hawkes process in solving the problem of the claim filing process in the nonlife insurance company. In the construction process, the excitation function with exponential decay is used. The result shows that the total likelihood function of the process depend on probability no claim at time t, the number of policyholder at time t, and the excitation function.